Object Detection - Science topic

Optical Character Recognition (OCR) in cursive scripts, where the letters of a word are joined in a flowing manner and overlap in both directions, deals with the struggles raised while segmentation of unrecognized characters and recognition of unseparated characters. In this paper, we propose using object detection models for character detection in...

Dear Colleagues, This Special Issue examines the essential functions of symmetry and asymmetry in image processing and computer vision, especially in relation to embedded systems. It seeks to compile pioneering research that demonstrates how these notions might improve algorithm efficiency, accuracy, and performance in real-time applications. Con...

Dear Colleagues, In AI and machine learning-based image processing, symmetry and asymmetry are fundamental properties that significantly impact pattern recognition, feature extraction, and algorithmic performance. This Special Issue, "Symmetry and Asymmetry in Artificial Intelligence and Machine Learning-Based Image Processing", explores how these...

In recent years, intelligent object detection technology has been applied to many industries. However, the high leakage rate of multiple defects and inefficient detection speed hinders the application of intelligent technology in steel surface defect detection tasks. Therefore, this paper proposes the RCD-YOLO model for solving the above problems....

The combination of computer vision and deep learning approaches has changed automated systems across numerous domains. Such a domain is object detection. This study presents an automatic boom gate access method based on the YOLOv8 (You Only Look Once version 8) object detection model and license plate recognition (LPR) technology. It tries to resol...

Background: The Kirby-Bauer disk diffusion method is a cost-effective and widely used technique for determining antimicrobial susceptibility, suitable for diverse laboratory settings. It involves placing antibiotic disks on a MuellerHinton agar plate inoculated with standardized bacteria, leading to inhibition zones after incubation. These zones ar...

To improve the accuracy and real-time performance of detection algorithms in Advanced Driver Assistance Systems (ADAS) under foggy conditions, this paper introduces HR-YOLO, an improved YOLO-based model specifically designed for vehicle and pedestrian detection. To enhance detection performance under complex meteorological conditions, several criti...

Concept drift in process mining occurs when a single event log includes data from multiple versions of a process, making the detection of such drifts essential for ensuring reliable process mining results. Although many techniques have been proposed, they exhibit limitations in accuracy and scope. Specifically, their accuracy diminishes when facing...

The YOLO (You Only Look Once) series has been a leading framework in real-time object detection, consistently improving the balance between speed and accuracy. However, integrating attention mechanisms into YOLO has been challenging due to their high computational overhead. YOLOv12 introduces a novel approach that successfully incorporates attentio...

Semantic 3D city models are worldwide easy-accessible, providing accurate, object-oriented, and semantic-rich 3D priors. To date, their potential to mitigate the noise impact on radar object detection remains under-explored. In this paper, we first introduce a unique dataset, RadarCity, comprising 54K synchronized radar-image pairs and semantic 3D...

In recent years, object detection in traffic scenes has gained significant traction and has become a crucial component of intelligent transportation systems. To bolster the robustness of object detection in traffic scenes, we propose an effective multiscale feature fusion network (EMFF-Net) inspired by the You Only Look Once (YOLO) architecture. We...

In UAV aerial imagery, small objects often occupy minimal pixel regions and are prone to being obscured by noisy backgrounds, posing significant detection challenges. Existing methods primarily rely on multi-scale information fusion to address these issues, yet their overall detection accuracy for small objects remains limited. To tackle this, we p...

Publicly available datasets for oil spill detection are scarce, making it difficult to compare the performance of different detection algorithms. To address this, this paper introduces a comprehensive labeled dataset of oil slicks, look-alikes, and other remarkable oceanic phenomena, derived from Sentinel-1 Synthetic Aperture Radar (SAR) products in...

Computer vision models have seen increased usage in sports, and reinforcement learning (RL) is famous for beating humans in strategic games such as Chess and Go. In this paper, we are interested in building upon these advances and examining the game of classic 8-ball pool. We introduce pix2pockets, a foundation for an RL-assisted pool coach. Given...

Purpose: Ensuring both human safety and transportation efficiency simultaneously during the navigation of autonomous mobile robots (AMRs) in warehouse logistics is a challenging problem due to dynamic environments and diverse obstacles. In this study, a social navigation approach based on artificial intelligence was developed to optimize these two...

In the field of autonomous driving, a commonly employed method to enhance detection accuracy and robustness is the fusion of multi-sensor perception. The fusion of millimeter-wave radar and camera can effectively complement each other, providing sufficient semantic information while ensuring robustness against varying illumination and weather condi...

X-ray image-based prohibited item detection plays a crucial role in modern public security systems. Despite significant advancements in deep learning, challenges such as feature extraction, object occlusion, and model complexity remain. Although recent efforts have utilized larger-scale CNNs or ViT-based architectures to enhance accuracy, these app...

Rapid and accurate detection of the maturity state of litchi fruits is crucial for orchard management and picking period prediction. However, existing studies are largely limited to the binary classification of immature and mature fruits, lacking dynamic evaluation and precise prediction of maturity states. To address these limitations, this study...

Low-light conditions pose significant challenges for both human and machine annotation. This in turn has led to a lack of research into machine understanding for low-light images and (in particular) videos. A common approach is to apply annotations obtained from high quality datasets to synthetically created low light versions. In addition, these a...

In this study, Cross-YOLO, an enhanced version of the YOLOv8 model, is specifically designed to address the challenge of detecting small objects in UAV target detection scenarios. The model refines the original YOLOv8 through several innovative improvements: Firstly, in order to improve the detection accuracy of small targets, we propose Cross-FPN...

Breast cancer has recently overtaken cervical cancer as the predominant cancer type in Indian urban areas. Despite considerable research and the development of automated diagnostic machines, current methods are far from perfect, necessitating more reliable medical assessments. Moreover, there's been relatively little research on Indian datasets com...

Posture is a critical phenotypic trait that reflects crop growth and serves as an essential indicator for both agricultural production and scientific research. Accurate pose estimation enables real-time tracking of crop growth processes, but in field environments, challenges such as variable backgrounds, dense planting, occlusions, and morphologica...

Planetary exploration demands effective image processing techniques for detecting anomalous objects and points of interest. However, technical constraints, such as limited spaceborne CPU performance and the growing need for compact, cost-effective robots, pose significant challenges. To address these issues, we propose a lightweight saliency map-ba...

The advancement of computer vision has led to significant improvements in image recognition, object detection, and video analysis. This paper focuses on designing efficient and accurate algorithms to enhance the performance of these tasks, which are critical in applications such as autonomous vehicles, medical imaging, and security systems. The pap...

The Logistics Objects in Context (LOCO) dataset is the first and only public dataset that integrates multiple logistic objects in a real logistics context. It is dedicated to object detection applications. However, the dataset is small and has unbalanced classes annotations problem. In this study, we present significant advancements to use YOLO mod...

Scene categorization is the dominant proxy for visual understanding, yet humans can perform a large number of visual tasks within any scene. Consequently, we know little about how different tasks change how a scene is processed, represented, and its features ultimately used. Here, we developed a novel brain-guided convolutional neural network (CNN)...

The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broader deep-learning landscape. This challenge is particularly acute in medical applications in developing countries with limited computational resources. Combining modern a...

RT-DETRs have shown strong performance across various computer vision tasks but are known to degrade under challenging weather conditions such as fog. In this work, we investigate three novel approaches to enhance RT-DETR robustness in foggy environments: (1) Domain Adaptation via Perceptual Loss, which distills domain-invariant features from a tea...

To address the high construction cost of datasets for object detection, particularly in industrial application scenarios where sufficient sample images cannot be obtained from the Internet due to the specialized nature and diversity of objects and their working environments, this paper proposes a method to automatically generate synthetic datasets...

Current object detectors often suffer significant perfor-mance degradation in real-world applications when encountering distributional shifts. Consequently, the out-of-distribution (OOD) generalization capability of object detectors has garnered increasing attention from researchers. Despite this growing interest, there remains a lack of a large-sc...

The purpose of infrared and visible fusion is to integrate useful information from both infrared and visible images into a single image. The fused image should possess rich texture details and salient target information of the two images. Current image fusion algorithms primarily face two limitations: 1) The lack of decoupling between modality-agno...

Current and future gravitational-wave observatories rely on large-scale, precision interferometers to detect the gravitational-wave signals. However, microscopic imperfections on the main interferometer mirrors, known as point absorbers, cause problematic heating of the optic via absorption of the high-power laser beam, which results in diminished...

This paper provides an extensive evaluation of YOLO object detection models (v5, v8, v9, v10, v11) by com- paring their performance across various hardware platforms and optimization libraries. Our study investigates inference speed and detection accuracy on Intel and AMD CPUs using popular libraries such as ONNX and OpenVINO, as well as on GPUs th...

In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance on OD has also revealed vulnerabilities to attacks, highlighting the need for effective detection methods to mitigate potential risks...

Moving object detection (MOD) in remote sensing is significantly challenged by low resolution, extremely small object sizes, and complex noise interference. Current deep learning-based MOD methods rely on probability density estimation, which restricts flexible information interaction between objects and across temporal frames. To flexibly capture...

Image segmentation has been a challenging issue in computer vision for years. In contrast to image classification and object detection, semantic segmentation is considered the top tier of the image analysis approach, which gives detailed details of the scene for a given input image. Analysis of aerial images without human intervention has developed...

Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitoring.However, existing models, including Convolutional Neural Networks (CNNs) and Transformers, struggle with these minute structures due to information loss.To address this, we introduce EFCNet, which...

Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based detection and segmentation, view...

In real-world scenarios, distribution shifts give rise to the importance of two problems: out-of-distribution (OoD) generalization, which focuses on models' generalization ability against covariate shifts (i.e., the changes of environments), and OoD detection, which aims to be aware of semantic shifts (i.e., test-time unseen classes). Real-world te...

Compared with the generic scenes, crowded scenes contain highly-overlapped instances, which result in: 1) more ambiguous anchors during training of object detectors, and 2) more predictions are likely to be mistakenly suppressed in post-processing during inference. To address these problems, we propose two new strategies, density-guided anchors (DG...

Cardiomegaly, or heart enlargement, is a serious condition in dogs that can lead to life threatening complications if not diagnosed early. Timely and accurate detection is essential for improving treatment outcomes and long term prognosis. This study presents a deep learning based method using the EfficientNet architecture to estimate the Vertebral...

Object detection is a key technology for marine exploration. The detection effect is not ideal because of factors such as the biodiversity and overlapping shadows in the underwater environment. Therefore, a new underwater object detection algorithm called RCF-YOLO is proposed. First, a coordinate enhancement (CE) attention module is designed. Depth...

Efficient image augmentation for hyperspectral satellite images requires design of multiband processing models that can assist in improving classification performance for different application scenarios. Existing models either work on dynamic band fusions, or use deep learning techniques for identification of application-specific augmentation opera...

In recent years, road traffic object detection has gained prominence in areas such as traffic monitoring, autonomous driving, and road safety. Nonetheless, existing algorithms offer room for improvement, particularly when detecting distant or inherently small targets, such as vehicles and pedestrians, from camera perspectives. By addressing the det...

With the rapid development of the ocean economy and the continuous in-depth development of ocean resources, object detection technology is becoming increasingly important in fields such as ocean scientific research, ecological protection, fisheries management, and ocean engineering. However, the complexity of the underwater background environment,...

This study presents the development and validation of an advanced object detection model based on YOLOv9 for the automated identification of dental caries. Utilizing a dataset of 270 dental images sourced from Kaggle, this project introduces a methodological framework that includes image preprocessing, augmentation, and annotation to address the ch...

The detection of waterborne pathogens is essential for safeguarding public health, especially in regions with limited access to clean and safe water. Traditional methods of microorganism detection often rely on time-consuming and resource-intensive laboratory techniques. To address this, the present study introduces an artificial intelligence-based...

Object detection precision is crucial for ensuring the safety and efficacy of autonomous driving systems. The quality of acquired images directly influences the ability of autonomous driving systems to correctly recognize and respond to other vehicles, pedestrians, and obstacles in real-time. However, real environments present extreme variations in...

Abnormal behavior detection in dense crowd, during the Hajj pilgrimage is vital to public security. Existing approaches face challenges due to factors like occlusions, illumination variations, and uniform attire. This research introduces the Crowd Anomaly Detection Framework (CADF), an improved YOLOv8-based model, integrating Soft-NMS to improve de...

Perception within autonomous driving is nearly synonymous with Neural Networks (NNs). Yet, the domain of autonomous racing is often characterized by scaled, computationally limited robots used for cost-effectiveness and safety. For this reason, opponent detection and tracking systems typically resort to traditional computer vision techniques due to...

The paper [Opt. Express 32, 40150 (2024)] investigates quantum illumination using polarization-entangled photon pairs for object detection in noisy environments. In this comment, we identify errors in the mathematical model for photon loss, particularly in the treatment of quantum entanglement under lossy channels. We show that the claimed robustne...

Machine vision enhances automation, quality control, and operational efficiency in industrial applications by enabling machines to interpret and act on visual data. While traditional computer vision algorithms and approaches remain widely utilized, machine learning has become pivotal in current research activities. In particular, generative Artific...

The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature space for novel categories with limited data on the basis of the base category space, which could adapt the learned detection model to unknown scenarios. Most existing fine...

n recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airbor...

This paper investigates the resilience of perception-based multi-robot coordination with wireless communication to online adversarial perception. A systematic study of this problem is essential for many safety-critical robotic applications that rely on the measurements from learned perception modules. We consider a (small) team of quadrotor robots...

Due to the limited capacity to capture low-probability features of abnormal objects in long-tail distributions, intelligent and efficient abnormal object detection still fails to achieve ideal performance in practical environments. Therefore, a lightweight method for abnormal object detection in transmission line corridors is proposed to address th...

Detection Transformer-based methods have achieved significant advancements in general object detection. However, challenges remain in effectively detecting small objects. One key difficulty is that existing encoders struggle to efficiently fuse low-level features. Additionally, the query selection strategies are not effectively tailored for small o...

Integration of Machine Learning (ML) techniques into public administration marks a new and transformative era for e-government systems. While traditionally e-government studies were focusing on text-based interactions, this one explores the innovative application of ML for image analysis, an approach that enables governments to address citizen peti...

Recent advancements in text-to-video (T2V) diffusion models have significantly enhanced the visual quality of the generated videos. However, even recent T2V models find it challenging to follow text descriptions accurately, especially when the prompt requires accurate control of spatial layouts or object trajectories. A recent line of research uses...

Underwater object detection remains a challenging task due to the presence of noise, lighting variations, and occlusions in underwater images. To address these challenges, this study proposes an improved underwater object detection model based on YOLOv9, integrating advanced attention mechanisms and a dilated large-kernel algorithm. Specifically, t...

Context. The problem of detecting deepfake audio has become increasingly critical with the rapid advancement of voice synthesis technologies and their potential for misuse. Traditional audio processing methods face significant challenges in distinguishing sophisticated deepfakes, particularly when tested across different types of audio manipulation...

Lightweight convolutional neural networks have created new opportunities for object recognition, enabling high-performance algorithms to operate on resource-constrained devices while preserving strong representational and generalization capabilities. This paper introduces a lightweight YOLO-Fast object detection model. By replacing the backbone fea...

Images captured under low-light conditions present significant limitations in many applications, as poor lighting can obscure details, reduce contrast, and hide noise. Removing the illumination effects and enhancing the quality of such images is crucial for many tasks, such as image segmentation and object detection. In this paper, we propose a var...

Using the picture data acquired by remote sensing technology, remote sensing image processing detection classifies ground objects. It identifies changes to help determine the state of natural and human activity on the earth’s surface. Owing to various obstacles in various application scenarios and the management of large amounts of data, most remot...

The timely and accurate detection of unidentified drones is crucial for public safety. However, challenges arise due to background noise in complex environments and limited feature representation of small, distant targets. Additionally, deep learning algorithms often demand substantial computational resources, limiting their use on low-capacity pla...

Current autonomous driving algorithms heavily rely on the visible spectrum, which is prone to performance degradation in adverse conditions like fog, rain, snow, glare, and high contrast. Although other spectral bands like near-infrared (NIR) and long-wave infrared (LWIR) can enhance vision perception in such situations, they have limitations and l...

The advancement of technology has revolutionised the agricultural industry, transitioning it from labour-intensive farming practices to automated, AI-powered management systems. In recent years, more intelligent livestock monitoring solutions have been proposed to enhance farming efficiency and productivity. This work presents a novel approach to a...

Object detection in Unmanned Aerial Vehicle (UAV) imagery plays an important role in many fields. However, UAV images usually exhibit characteristics different from those of natural images, such as complex scenes, dense small targets, and significant variations in target scales, which pose considerable challenges for object detection tasks. To addr...

The proposed work introduces OD3-YOLO for the detection of the glaucoma and diabetic retinopathy directly from the detection of the optic disc in retinal fundus images. The work employs the use of three publicly available datasets of SMDG, IDRID, and REFUGE consisting of retinal fundus imagery for glaucoma, diabetic retinopathy, and normal eye; fol...

The rapid economic growth and heightened social activities have substantially increased road usage, leading to a higher incidence of road cracks. An efficient automated method for detecting and locating these cracks are crucial for mitigating traffic safety risks. However, existing object detection algorithms often struggle with large model paramet...

Despite the success of deep learning in close-set 3D object detection, existing approaches struggle with zero-shot generalization to novel objects and camera configurations. We introduce DetAny3D, a promptable 3D detection foundation model capable of detecting any novel object under arbitrary camera configurations using only monocular inputs. Train...

In challenging underwater environments with limited visibility due to poor lighting and murky water, the ability to detect and identify targets is essential for various marine operations. We present an improved YOLO11 network, called YOLO-CE, to increase the accuracy of underwater target recognition under such circumstances. First, we propose a new...

Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-based reward formulation, which leverages tasks with deterministic ground-truth answers to enable precise and stable reward comput...

Under dynamic conditions, star spots will move on the image plane of the star sensor, resulting in trailing of the star map. This trailing can significantly reduce the accuracy of star centroid positioning, thereby affecting satellite attitude determination. Unlike traditional methods that restore blurred star maps before positioning, we treat the...

Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be d...

This paper studies a lightweight construction safety behavior detection model based on improved YOLOv8, aiming to improve the detection accuracy of safety behaviors on construction sites and achieve lightweight models. YOLO (You Only Look Once) is an object detection algorithm that can achieve real-time and efficient object detection by dividing im...

Motivated by the growing demand for low-precision arithmetic in computational science, we exploit lower-precision emulation in Python -- widely regarded as the dominant programming language for numerical analysis and machine learning. Low-precision training has revolutionized deep learning by enabling more efficient computation and reduced memory a...

Fabric defect detection is crucial in the textile industry, as it suffers from challenges such as small defect sizes, diverse morphologies, and imbalanced sample distributions. Current mainstream methods approach it as an object detection problem. Many fabric defects, particularly small ones, are caused by production faults that disrupt the fabric...

The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature space for novel categories with limited data on the basis of the base category space, which could adapt the learned detection model to unknown scenarios. However, limited b...

The issue of potholes and bad road quality is faced by nearly every developing country. These conditions often lead to accidents and traffic and cause general public inconvenience. Significant research has been performed in the field of effective pothole and road quality detection, but owing to several underlying issues such as relying on only comp...